Faster Training Algorithms for Structured Sparsity-Inducing Norm
Authors: Bin Gu, Xingwang Ju, Xiang Li, Guansheng Zheng
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that our algorithm is much more efficient than the network-flow algorithm, while retaining the similar generalization performance. |
| Researcher Affiliation | Academia | School of Computer & Software, Nanjing University of Information Science & Technology, P.R.China Department of Electrical & Computer Engineering, University of Pittsburgh, USA Computer Science Department, University of Western Ontario, Canada |
| Pseudocode | Yes | Algorithm 1 Inexact Subgradient Descent Algorithm |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | The Nartual, Tesdata, Yearst, Sector and Realsim datasets are from http://www.mldata.org/ repository/data/. The Coil20 dataset is from http: //www.cs.columbia.edu/CAVE/software/. The Movielen100k dataset is from http://archive.ics. uci.edu/ml/datasets.html. |
| Dataset Splits | No | The paper lists datasets used but does not provide specific train/validation/test dataset splits or their percentages/counts. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory, or processing units used for running the experiments. |
| Software Dependencies | No | The paper states that the algorithms are implemented in 'MATLAB' but does not provide a specific version number or other software dependencies with their versions. |
| Experiment Setup | Yes | In experiments, the outer loop iteration is selected from {300, 500, 1000} to guarantee convergence. The value of stepsize γ is selected from 10 3, 10 4, 10 5, 10 6 to satisfy γ 1 L. The λ is set 0.1. The initial vector xi has 20% percent nonzero components, randomly selected, and uniformly generated between [ 1, 1] for normalization. The weight ηg for each group is also randomly generated between [0, 1]. |